Initial commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,77 @@
+---
+library_name: stable-baselines3
+tags:
+- BipedalWalkerHardcore-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: TD3
+ results:
+ - task:
+ type: reinforcement-learning
+ name: reinforcement-learning
+ dataset:
+ name: BipedalWalkerHardcore-v3
+ type: BipedalWalkerHardcore-v3
+ metrics:
+ - type: mean_reward
+ value: -24.80 +/- 3.78
+ name: mean_reward
+ verified: false
+---
+
+# **TD3** Agent playing **BipedalWalkerHardcore-v3**
+This is a trained model of a **TD3** agent playing **BipedalWalkerHardcore-v3**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
+and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
+
+The RL Zoo is a training framework for Stable Baselines3
+reinforcement learning agents,
+with hyperparameter optimization and pre-trained agents included.
+
+## Usage (with SB3 RL Zoo)
+
+RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
+SB3: https://github.com/DLR-RM/stable-baselines3<br/>
+SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
+
+Install the RL Zoo (with SB3 and SB3-Contrib):
+```bash
+pip install rl_zoo3
+```
+
+```
+# Download model and save it into the logs/ folder
+python -m rl_zoo3.load_from_hub --algo td3 --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo td3 --env BipedalWalkerHardcore-v3 -f logs/
+```
+
+If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
+```
+python -m rl_zoo3.load_from_hub --algo td3 --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo td3 --env BipedalWalkerHardcore-v3 -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo td3 --env BipedalWalkerHardcore-v3 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo td3 --env BipedalWalkerHardcore-v3 -f logs/ -orga qgallouedec
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 256),
+ ('buffer_size', 1000000),
+ ('gamma', 0.99),
+ ('learning_rate', 'lin_7e-4'),
+ ('learning_starts', 10000),
+ ('n_timesteps', 10000000.0),
+ ('noise_std', 0.1),
+ ('noise_type', 'normal'),
+ ('policy', 'MlpPolicy'),
+ ('policy_kwargs', 'dict(net_arch=[400, 300])'),
+ ('train_freq', 1),
+ ('normalize', False)])
+```

args.yml

@@ -0,0 +1,81 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+ - td3
+ - - conf_file
+ - null
+ - - device
+ - auto
+ - - env
+ - BipedalWalkerHardcore-v3
+ - - env_kwargs
+ - null
+ - - eval_episodes
+ - 5
+ - - eval_freq
+ - 25000
+ - - gym_packages
+ - []
+ - - hyperparams
+ - null
+ - - log_folder
+ - logs
+ - - log_interval
+ - -1
+ - - max_total_trials
+ - null
+ - - n_eval_envs
+ - 1
+ - - n_evaluations
+ - null
+ - - n_jobs
+ - 1
+ - - n_startup_trials
+ - 10
+ - - n_timesteps
+ - -1
+ - - n_trials
+ - 500
+ - - no_optim_plots
+ - false
+ - - num_threads
+ - -1
+ - - optimization_log_path
+ - null
+ - - optimize_hyperparameters
+ - false
+ - - progress
+ - false
+ - - pruner
+ - median
+ - - sampler
+ - tpe
+ - - save_freq
+ - -1
+ - - save_replay_buffer
+ - false
+ - - seed
+ - 3214139235
+ - - storage
+ - null
+ - - study_name
+ - null
+ - - tensorboard_log
+ - runs/BipedalWalkerHardcore-v3__td3__3214139235__1672169167
+ - - track
+ - true
+ - - trained_agent
+ - ''
+ - - truncate_last_trajectory
+ - true
+ - - uuid
+ - false
+ - - vec_env
+ - dummy
+ - - verbose
+ - 1
+ - - wandb_entity
+ - openrlbenchmark
+ - - wandb_project_name
+ - sb3
+ - - yaml_file
+ - null

config.yml

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+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+ - 256
+ - - buffer_size
+ - 1000000
+ - - gamma
+ - 0.99
+ - - learning_rate
+ - lin_7e-4
+ - - learning_starts
+ - 10000
+ - - n_timesteps
+ - 10000000.0
+ - - noise_std
+ - 0.1
+ - - noise_type
+ - normal
+ - - policy
+ - MlpPolicy
+ - - policy_kwargs
+ - dict(net_arch=[400, 300])
+ - - train_freq
+ - 1

env_kwargs.yml

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+{}

replay.mp4

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results.json

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+{"mean_reward": -24.8020791, "std_reward": 3.7823493281860796, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-27T16:26:13.705394"}

td3-BipedalWalkerHardcore-v3.zip

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td3-BipedalWalkerHardcore-v3/_stable_baselines3_version

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+1.8.0a6

td3-BipedalWalkerHardcore-v3/actor.optimizer.pth

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td3-BipedalWalkerHardcore-v3/critic.optimizer.pth

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td3-BipedalWalkerHardcore-v3/data

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